wehub-resource-sync
918 lines
33 KiB
Python
918 lines
33 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import convert_float_to_uint16, convert_uint16_to_float
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from op_test_xpu import XPUOpTest
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddle.base import Executor, Program, default_main_program, program_guard
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paddle.enable_static()
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class XPUTestPad3dOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'pad3d'
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class TestPad3dOp(XPUOpTest):
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def setUp(self):
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paddle.enable_static()
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self.op_type = "pad3d"
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.value = 0.0
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self.initTestCase()
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self.python_api = paddle.nn.functional.pad
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self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
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self.attrs = {}
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if self.variable_paddings:
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self.attrs['paddings'] = []
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self.inputs['Paddings'] = (
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np.array(self.paddings).flatten().astype("int32")
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)
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else:
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self.attrs['paddings'] = (
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np.array(self.paddings).flatten().astype("int32")
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)
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self.attrs['value'] = self.value
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self.attrs['mode'] = self.mode
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self.attrs['data_format'] = self.data_format
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if self.data_format == "NCDHW":
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paddings = [
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(0, 0),
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(0, 0),
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(self.paddings[4], self.paddings[5]),
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(self.paddings[2], self.paddings[3]),
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(self.paddings[0], self.paddings[1]),
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]
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else:
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paddings = [
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(0, 0),
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(self.paddings[4], self.paddings[5]),
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(self.paddings[2], self.paddings[3]),
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(self.paddings[0], self.paddings[1]),
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(0, 0),
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]
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if self.mode == "constant":
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out = np.pad(
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self.inputs['X'],
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paddings,
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mode=self.mode,
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constant_values=self.value,
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)
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elif self.mode == "reflect":
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out = np.pad(self.inputs['X'], paddings, mode=self.mode)
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elif self.mode == "replicate":
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out = np.pad(self.inputs['X'], paddings, mode="edge")
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elif self.mode == "circular":
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out = np.pad(self.inputs['X'], paddings, mode="wrap")
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(check_dygraph=True)
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', check_dygraph=True)
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 0, 0, 0, 0, 0]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.pad_value = 0.0
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self.variable_paddings = False
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class TestCase1(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.value = 1.0
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self.variable_paddings = False
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class TestCase2(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [1, 1, 1, 1, 1, 1]
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self.mode = "constant"
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self.data_format = "NDHWC"
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self.value = 1.0
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self.variable_paddings = False
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class TestCase3(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 1, 0, 2, 3]
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self.mode = "reflect"
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self.data_format = "NCDHW"
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self.variable_paddings = False
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class TestCase4(TestPad3dOp):
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def initTestCase(self):
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self.shape = (4, 4, 4, 4, 4)
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self.paddings = [0, 1, 2, 1, 2, 3]
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self.mode = "reflect"
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self.data_format = "NDHWC"
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self.variable_paddings = False
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class TestCase5(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 2, 1]
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self.mode = "replicate"
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self.data_format = "NCDHW"
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self.variable_paddings = False
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class TestCase6(TestPad3dOp):
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def initTestCase(self):
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self.shape = (4, 4, 4, 4, 4)
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self.paddings = [5, 4, 2, 1, 2, 3]
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self.mode = "replicate"
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self.data_format = "NDHWC"
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self.variable_paddings = False
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class TestCase7(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.value = 1.0
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self.variable_paddings = True
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class TestCase8(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NDHWC"
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self.value = 1.0
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self.variable_paddings = True
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class TestPadAPI(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.XPUPlace(0)]
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self.dtype = self.in_type
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def check_static_result_1(self, place):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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input_shape = (1, 2, 3, 4, 5)
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pad = [1, 2, 1, 1, 3, 4]
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mode = "constant"
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result = F.pad(
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x=x, pad=pad, value=value, mode=mode, data_format="NCDHW"
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)
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exe = Executor(place)
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fetches = exe.run(
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default_main_program(),
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feed={"x": input_data},
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fetch_list=[result],
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)
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np_out = self._get_numpy_out(input_data, pad, mode, value)
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np.testing.assert_allclose(fetches[0], np_out, rtol=1e-05)
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def check_static_result_2(self, place):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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input_shape = (2, 3, 4, 5, 6)
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pad = [1, 2, 1, 1, 1, 2]
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mode = "reflect"
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW")
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result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC")
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exe = Executor(place)
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fetches = exe.run(
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default_main_program(),
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feed={"x": input_data},
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fetch_list=[result1, result2],
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)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, data_format="NCDHW"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, data_format="NDHWC"
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)
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np.testing.assert_allclose(fetches[0], np_out1, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], np_out2, rtol=1e-05)
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def check_static_result_3(self, place):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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input_shape = (2, 3, 4, 5, 6)
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pad = [1, 2, 1, 1, 3, 4]
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mode = "replicate"
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW")
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result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC")
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exe = Executor(place)
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fetches = exe.run(
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default_main_program(),
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feed={"x": input_data},
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fetch_list=[result1, result2],
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)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, data_format="NCDHW"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, data_format="NDHWC"
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)
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np.testing.assert_allclose(fetches[0], np_out1, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], np_out2, rtol=1e-05)
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def _get_numpy_out(
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self, input_data, pad, mode, value=0, data_format="NCDHW"
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):
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if mode == "constant" and len(pad) == len(input_data.shape) * 2:
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pad = np.reshape(pad, (-1, 2)).tolist()
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elif data_format == "NCDHW":
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pad = [
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(0, 0),
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(0, 0),
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(pad[4], pad[5]),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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]
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elif data_format == "NDHWC":
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pad = [
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(0, 0),
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(pad[4], pad[5]),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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(0, 0),
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]
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elif data_format == "NCHW":
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pad = [
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(0, 0),
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(0, 0),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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]
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elif data_format == "NHWC":
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pad = [
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(0, 0),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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(0, 0),
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]
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elif data_format == "NCL":
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pad = [
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(0, 0),
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(0, 0),
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(pad[0], pad[1]),
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]
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elif data_format == "NLC":
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pad = [
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(0, 0),
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(pad[0], pad[1]),
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(0, 0),
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]
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if self.dtype == np.uint16:
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input_data = convert_uint16_to_float(input_data)
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if mode == "constant":
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out = np.pad(input_data, pad, mode=mode, constant_values=value)
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elif mode == "reflect":
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out = np.pad(input_data, pad, mode=mode)
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elif mode == "replicate":
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out = np.pad(input_data, pad, mode="edge")
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elif mode == "circular":
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out = np.pad(input_data, pad, mode="wrap")
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if self.dtype == np.uint16:
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out = convert_float_to_uint16(out)
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return out
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def test_static(self):
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for place in self.places:
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self.check_static_result_1(place=place)
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self.check_static_result_2(place=place)
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self.check_static_result_3(place=place)
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def test_dygraph_1(self):
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# TODO: remove fp16 limit after support of pad op
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if self.dtype == np.float16:
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return
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paddle.disable_static()
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input_shape = (1, 2, 3, 4, 5)
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pad = [1, 2, 1, 1, 3, 4]
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pad_3 = [1, 2, 1, 1, 3, 4, 5, 6, 7, 8]
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mode = "constant"
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NCDHW"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NDHWC"
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)
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np_out3 = self._get_numpy_out(
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input_data, pad_3, mode, value, data_format="NCDHW"
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)
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tensor_data = paddle.to_tensor(input_data)
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y1 = F.pad(
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tensor_data,
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pad=pad,
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mode=mode,
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value=value,
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data_format="NCDHW",
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)
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y2 = F.pad(
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tensor_data,
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pad=pad,
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mode=mode,
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value=value,
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data_format="NDHWC",
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)
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y3 = F.pad(
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tensor_data,
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pad=pad_3,
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mode=mode,
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value=value,
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data_format="NCDHW",
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)
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np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
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np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
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np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
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def test_dygraph_2(self):
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# TODO: remove fp16 limit after support of pad op
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if self.dtype == np.float16:
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return
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paddle.disable_static()
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input_shape = (2, 3, 4, 5)
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pad = [1, 1, 3, 4]
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pad_3 = [1, 2, 1, 1, 3, 4, 5, 6]
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mode = "constant"
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NCHW"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NHWC"
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)
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np_out3 = self._get_numpy_out(
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input_data, pad_3, mode, value, data_format="NCHW"
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)
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tensor_data = paddle.to_tensor(input_data)
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tensor_pad = paddle.to_tensor(pad, dtype="int32")
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y1 = F.pad(
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tensor_data,
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pad=tensor_pad,
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mode=mode,
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value=value,
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data_format="NCHW",
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)
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y2 = F.pad(
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tensor_data,
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pad=tensor_pad,
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mode=mode,
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value=value,
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data_format="NHWC",
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)
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y3 = F.pad(
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tensor_data,
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pad=pad_3,
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mode=mode,
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value=value,
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data_format="NCHW",
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)
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np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
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np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
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np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
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def test_dygraph_3(self):
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# TODO: remove fp16 limit after support of pad op
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if self.dtype == np.float16:
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return
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paddle.disable_static()
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input_shape = (3, 4, 5)
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pad = [3, 4]
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pad_3 = [3, 4, 5, 6, 7, 8]
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mode = "constant"
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NCL"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, value, data_format="NLC"
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)
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np_out3 = self._get_numpy_out(
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input_data, pad_3, mode, value, data_format="NCL"
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)
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tensor_data = paddle.to_tensor(input_data)
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tensor_pad = paddle.to_tensor(pad, dtype="int32")
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y1 = F.pad(
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tensor_data,
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pad=tensor_pad,
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mode=mode,
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value=value,
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data_format="NCL",
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)
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y2 = F.pad(
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tensor_data,
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pad=tensor_pad,
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mode=mode,
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value=value,
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data_format="NLC",
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)
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y3 = F.pad(
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tensor_data,
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pad=pad_3,
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mode=mode,
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value=value,
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data_format="NCL",
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)
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np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
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np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
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np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
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|
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class TestPad1dAPI(unittest.TestCase):
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def _get_numpy_out(
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self, input_data, pad, mode, value=0.0, data_format="NCL"
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):
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if data_format == "NCL":
|
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pad = [
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(0, 0),
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(0, 0),
|
|
(pad[0], pad[1]),
|
|
]
|
|
else:
|
|
pad = [
|
|
(0, 0),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if self.dtype == np.uint16:
|
|
input_data = convert_uint16_to_float(input_data)
|
|
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
if self.dtype == np.uint16:
|
|
out = convert_float_to_uint16(out)
|
|
|
|
return out
|
|
|
|
def setUp(self):
|
|
self.places = [paddle.XPUPlace(0)]
|
|
self.dtype = self.in_type
|
|
|
|
def test_class(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5)
|
|
pad = [1, 2]
|
|
pad_int = 1
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
|
|
pad_reflection = nn.Pad1D(padding=pad, mode="reflect")
|
|
pad_replication = nn.Pad1D(padding=pad, mode="replicate")
|
|
pad_constant = nn.Pad1D(
|
|
padding=pad, mode="constant", value=value
|
|
)
|
|
pad_constant_int = nn.Pad1D(
|
|
padding=pad_int, mode="constant", value=value
|
|
)
|
|
pad_circular = nn.Pad1D(padding=pad, mode="circular")
|
|
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_replication(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "replicate", data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "constant", value=value, data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant_int(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
[pad_int] * 2,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCL",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
class TestPad2dAPI(unittest.TestCase):
|
|
def _get_numpy_out(
|
|
self, input_data, pad, mode, value=0.0, data_format="NCHW"
|
|
):
|
|
if data_format == "NCHW":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
]
|
|
else:
|
|
pad = [
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if self.dtype == np.uint16:
|
|
input_data = convert_uint16_to_float(input_data)
|
|
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
if self.dtype == np.uint16:
|
|
out = convert_float_to_uint16(out)
|
|
|
|
return out
|
|
|
|
def setUp(self):
|
|
self.places = [paddle.XPUPlace(0)]
|
|
self.dtype = self.in_type
|
|
|
|
def test_class(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5, 6)
|
|
pad = [1, 2, 2, 1]
|
|
pad_int = 1
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
|
|
pad_reflection = nn.Pad2D(padding=pad, mode="reflect")
|
|
pad_replication = nn.Pad2D(padding=pad, mode="replicate")
|
|
pad_constant = nn.Pad2D(
|
|
padding=pad, mode="constant", value=value
|
|
)
|
|
pad_constant_int = nn.Pad2D(
|
|
padding=pad_int, mode="constant", value=value
|
|
)
|
|
pad_circular = nn.Pad2D(padding=pad, mode="circular")
|
|
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_replication(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "replicate", data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "constant", value=value, data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant_int(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
[pad_int] * 4,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCHW",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
class TestPad3dAPI(unittest.TestCase):
|
|
def _get_numpy_out(
|
|
self, input_data, pad, mode, value=0.0, data_format="NCDHW"
|
|
):
|
|
if data_format == "NCDHW":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[4], pad[5]),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
]
|
|
else:
|
|
pad = [
|
|
(0, 0),
|
|
(pad[4], pad[5]),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if self.dtype == np.uint16:
|
|
input_data = convert_uint16_to_float(input_data)
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
if self.dtype == np.uint16:
|
|
out = convert_float_to_uint16(out)
|
|
|
|
return out
|
|
|
|
def setUp(self):
|
|
self.places = [paddle.XPUPlace(0)]
|
|
self.dtype = self.in_type
|
|
|
|
def test_class(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5, 6, 7)
|
|
pad = [1, 2, 2, 1, 1, 0]
|
|
pad_int = 1
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
|
|
pad_reflection = nn.Pad3D(padding=pad, mode="reflect")
|
|
pad_replication = nn.Pad3D(padding=pad, mode="replicate")
|
|
pad_constant = nn.Pad3D(
|
|
padding=pad, mode="constant", value=value
|
|
)
|
|
pad_constant_int = nn.Pad3D(
|
|
padding=pad_int, mode="constant", value=value
|
|
)
|
|
pad_circular = nn.Pad3D(padding=pad, mode="circular")
|
|
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCDHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_replication(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "replicate", data_format="NCDHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
pad,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCDHW",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant_int(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
[pad_int] * 6,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCDHW",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
def test_pad_tensor(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5, 6, 7)
|
|
pad = [1, 2, 2, 1, 1, 0]
|
|
pad_tensor = paddle.to_tensor(pad)
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
|
|
pad_reflection_ncdhw = nn.Pad3D(
|
|
padding=pad_tensor, mode="reflect", data_format="NCDHW"
|
|
)
|
|
pad_reflection_ndhwc = nn.Pad3D(
|
|
padding=pad_tensor, mode="reflect", data_format="NDHWC"
|
|
)
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection_ncdhw(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCDHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_reflection_ndhwc(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NDHWC"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
class TestPad3dOpError(unittest.TestCase):
|
|
def setUp(self):
|
|
self.places = [paddle.XPUPlace(0)]
|
|
self.dtype = self.in_type
|
|
|
|
def test_errors(self):
|
|
def test_variable():
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
y = F.pad(x=data, pad=[1, 1, 1, 1, 1, 1], data_format="NCDHW")
|
|
|
|
def test_reflect_1():
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
x = paddle.to_tensor(data)
|
|
y = F.pad(
|
|
x,
|
|
pad=[5, 6, 1, 1, 1, 1],
|
|
value=1,
|
|
mode='reflect',
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
def test_reflect_2():
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
x = paddle.to_tensor(data)
|
|
y = F.pad(
|
|
x,
|
|
pad=[1, 1, 4, 3, 1, 1],
|
|
value=1,
|
|
mode='reflect',
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
def test_reflect_3():
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
x = paddle.to_tensor(data)
|
|
y = F.pad(
|
|
x,
|
|
pad=[1, 1, 1, 1, 2, 3],
|
|
value=1,
|
|
mode='reflect',
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
def test_replicate_1():
|
|
input_shape = (1, 2, 0, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
x = paddle.to_tensor(data)
|
|
y = F.pad(
|
|
x,
|
|
pad=[1, 1, 1, 1, 2, 3],
|
|
mode='replicate',
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
paddle.disable_static()
|
|
for _ in self.places:
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"pad3d\(\): argument 'x' \(position 0\) must be Tensor, but got numpy.ndarray",
|
|
test_variable,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The width of Input\(X\)'s dimension should be greater than pad_left in reflect mode",
|
|
test_reflect_1,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The height of Input\(X\)'s dimension should be greater than pad_top in reflect mode",
|
|
test_reflect_2,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The depth of Input\(X\)'s dimension should be greater than pad_back in reflect mode",
|
|
test_reflect_3,
|
|
)
|
|
# comment out because pad3d support 0-size now.
|
|
# self.assertRaises(Exception, test_replicate_1)
|
|
paddle.enable_static()
|
|
|
|
class TestPadDataformatError(unittest.TestCase):
|
|
def test_errors(self):
|
|
def test_ncl():
|
|
input_shape = (1, 2, 3, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(
|
|
padding=pad, mode="replicate", data_format="NCL"
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
def test_nchw():
|
|
input_shape = (1, 2, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(
|
|
padding=pad, mode="replicate", data_format="NCHW"
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
def test_ncdhw():
|
|
input_shape = (1, 2, 3, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(
|
|
padding=pad, mode="replicate", data_format="NCDHW"
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
self.assertRaises(AssertionError, test_ncl)
|
|
self.assertRaises(AssertionError, test_nchw)
|
|
self.assertRaises(AssertionError, test_ncdhw)
|
|
|
|
|
|
support_types = get_xpu_op_support_types('pad3d')
|
|
for stype in support_types:
|
|
create_test_class(globals(), XPUTestPad3dOp, stype)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|